The present invention relates generally to a floating gate setup method applicable on an non-volatile memory (NVM) floating gate, and more particularly, but not by way of limitation, to a system, method, and computer program product for setting up all of the weights (e.g., stored charge and threshold value of floating gate) in the layers and also on other layers of the floating gate.
Neural Networks are the leading method to implement Machine learning and training for cognitive computing. Neural Networks can be implemented at a software level but more efficiently in hardware such as with a cross bar implementation with variable resistance as a weighting mechanism. Many type of weights have been proposed. None of the proposed weights is best during training since they are not symmetric. However, for inference purposes, the weights need to be updated once and do not have to be symmetric. Inference has to be cheap and low power to be installed in an end user system such as a smart phone, automotive, Internet-of-Things (IoT) devices, camera, etc.